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Managerial Aspects of Enterprise Risk Management
David L. OlsonUniversity of Nebraska-Lincoln
Desheng WuUniversity of Toronto; University of Reykjavik
Risk & Business
• Taking risk is fundamental to doing business– Insurance
• Lloyd’s of London– Hedging
• Risk exchange swaps• Derivatives/options• Catastrophe equity puts (cat-e-puts)
– ERM seeks to rationally manage these risks• Be a Risk Shaper
Risk Reduction StrategiesC.S. Tang
Journal of Logistics: Research and Applications 9:1 [2006] 33-45
1. Identify different types of risk2. Estimate likelihood of each event3. Assess potential loss from major disruption4. Identify strategies to reduce risk
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Another viewSlywotzky & Drzik, HBR [2005]
• Financial– Currency fluctuation
• DEFENSE: Hedging• Hazard
– Chemical spill• DEFENSE: Insurance
• Operational– Computer system failure
• DEFENSE: Backup (dispersion, firewalls)• New technology overtaking your product
– ACE inhibitors, calcium channel blockers ate into hypertension drug market of beta-blockers & diuretics
• Demand shifts– Gradual – Oldsmobile; Rapid - Station wagons to Minivans
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Technology Shift
• Loss of patent protection• Outdated manufacturing process
– DEFENSE: Double bet• Invest in multiple versions of technology• Microsoft: OS/2 & Windows• Intel: RISC & CISC• Motorola didn’t – Nokia, Samsung entered
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Brand Erosion
• Perrier – contamination• Firestone – Ford Explorer• GM Saturn – not enough new models
– DEFENSE: Redefine scope• Emphasize service, quality
– DEFENSE: Reallocate brand investment• AMEX – responded to VISA campaign, reduced
transaction fees, sped up payments, more ads
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One-of-a-kind Competitor
• Competitor redefines market• Wal-Mart
– DEFENSE: Create new, non-overlapping business design
• Target – unique product selection
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Customer Priority Shift
– DEFENSE: Analyze proprietary information• Identify next customer shift
– Coach leather goods – competes with Gucci– Went trendy, aggressive in-market testing
» Customer interviews, in-store product tests
– DEFENSE: Market experiments• Capital One – 65,000 experiments annually
– Identify ever-smaller customer segments for credit cards
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New Project Failure
• Edsel – DEFENSE: Initial analysis
• Best defense– DEFENSE: Smart sequencing
• Do better-controllable projects first– Applied Materials – chip-making
– DEFENSE: Develop excess options• Improve odds of eventual success
– Toyota – hybrid: proliferation of Prius options
– DEFENSE: Stepping-stone method• Create series of projects
– Toyota – rolling out Prius
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DEALING WITH RISK
• Management responsible for ALL risks facing an organization
• CANNOT POSSIBLY EXPECT TO ANTICIPATE ALL• AVOID SEEKING OPTIMAL PROFIT THROUGH
ARBITRAGE• FOCUS ON CONTINGENCY PLANNING
– CONSIDER MULTIPLE CRITERIA– MISTRUST MODELS
Financial Risk Management
• Evaluate chance of loss– PLAN
• Hubbard [2009]: identification, assessment, prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, and control the probability and/or impact of unfortunate events– WATCH, DO SOMETHING
Value-at-Risk
• One of most widely used models in financial risk management (Gordon [2009])
• Maximum expected loss over given time horizon at given confidence level– Typically how much would you expect to lose 99%
of the time over the next day (typical trading horizon)
• Implication – will do worse (1-0.99) proportion of the time
VaR = 0.64expect to exceed 99% of time in 1 year
Here loss = 10 – 0.64 = 9.36
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Use• Basel Capital Accord
– Banks encouraged to use internal models to measure VaR
– Use to ensure capital adequacy (liquidity)– Compute daily at 99th percentile
• Can use others– Minimum price shock equivalent to 10 trading days
(holding period)– Historical observation period ≥1 year– Capital charge ≥ 3 x average daily VaR of last 60
business days
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Limits
• At 99% level, will exceed 3-4 times per year• Distributions have fat tails• Only considers probability of loss – not
magnitude• Conditional Value-At-Risk
– Weighted average between VaR & losses exceeding VaR
– Aim to reduce probability a portfolio will incur large losses
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Correlation Makes a DifferenceDaily Models t-distribution
Correlation impact on VarianceDaily Models t-distribution
3 outliers – China mixed with others
Correlation impact on Value-at-RiskDaily Models t-distribution
Directly proportional to Variance
Conclusions
• Can use a variety of models to plan portfolio• Expect results to be jittery
– Near-optimal may turn out better– Sensitive to distribution assumed
• Trade-off – risk & return
COSOCommittee of Sponsoring Organizations
Treadway Committee – 1990sSmiechewicz [2001]
• Assign responsibility– Board of directors
• Establish organization’s risk appetite• establish audit & risk management policies
– Executives assume ownership• Policies express position on integrity, ethics• Responsibilities for insurance, auditing, loan review, credit, legal
compliance, quality, security
• Common language– Risk definitions specific to organization
• Value-adding framework
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COSO Integrated Framework 2004Levinsohn [2004]; Bowling & Rieger [2005]
• Internal environment – describe domain• Objective setting – objectives consistent with
mission, risk appetite• Event identification – risks/opportunities• Risk assessment - analysis• Risk response – based on risk tolerance & appetite• Control activities• Information & communication – to responsible
people• Monitoring
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Supply Chain Risk Categories6 sources
CATEGORY RISK
NATURE External Natural disaster, plant fire, disease & epidemics
POLITICAL SYSTEM “ War, terrorism, labor disputes, regulations
COMPETITOR & MARKET “ Price, recession, exchange rateDemand, customer paymentNew technology, obsolescence substitutes
AVAILABLE CAPACITY Internal Capacity cost, supplier bankruptcy
INTERNAL OPERATION “ Forecast inaccuracy, safetyBullwhip, agility, on-time deliveryTradeoff: inventory/fill rateQuality
INFORMATION SYSTEM “ System breakdownDistorted informationIntegrationViruses/bugs/hackers
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Supply Chain risk management processP. Chapman, M. Cristopher, U. Juttner, H. Peck, R. Wilding,
Logistics and Transportation Focus 4:4 [2002] 59-64• Risk Identification
– Uncertainties: demand, supply, cost {quantitative}– Disruption: disasters, economic crises {qualitative}
• Risk Assessment– Political– Product availability– Capacity, demand fluctuation– Technology, labor– Financial instability, management turnover
• Risk Avoidance– Insurance– Inventory buffers– Supply chain alliances, e-procurement
• Risk Mitigation– Product pricing, other demand control– Product variety– VMI, CPFR
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Empirical
• BUBBLES– Dutch tulip mania – early 17th Century– South Sea Company – 1711-1720– Mississippi Company – 1719-1720
• Isaac Newton got burned: “I can calculate the motion of heavenly bodies but not the madness of people.”
Modern Bubbles
• London Market Exchange (LMX) spiral– 1983 excess-of-loss reinsurance popular– Syndicates ended up paying themselves to insure
themselves against ruin– Viewed risks as independent
• WEREN’T: hedging cycle among same pool of insurers
– Hurricane Alicia in 1983 stretched the system
Long Term Capital Management
• Black-Scholes – model pricing derivatives• LTCM formed to take advantage
– Heavy cost to participate– Did fabulously well
• 1998 invested in Russian banks– Russian banks collapsed– LTCM bailed out by US Fed
• LTCM too big to allow to collapse
Information Technology
• 1990s very hot profession• Venture capital threw money at Internet ideas
– Stock prices skyrocketed– IPOs made many very rich nerds– Most failed
• 2002 bubble burst– IT industry still in trouble
• ERP, outsourcing
Real Estate
• Considered safest investment around– 1981 deregulation
• In some places (California) consistent high rates of price inflation– Banks eager to invest in mortgages – created tranches of
mortgage portfolios• 2008 – interest rates fell
– Soon many risky mortgages cost more than houses worth– SUBPRIME MORTGAGE COLLAPSE– Risk avoidance system so interconnected that most
banks at risk
APPROACHES TO THE PROBLEM
• MAKE THE MODELS BETTER– The economic theoretical way– But human systems too complex to completely
capture– Black-Scholes a good example
• PRACTICAL ALTERNATIVES– Buffett– Soros
Better ModelsCooper [2008]
• Efficient market hypothesis – Inaccurate description of real markets– disregards bubbles
• FAT TAILS• Hyman Minsky [2008]
– Financial instability hypothesis• Markets can generate waves of credit expansion, asset inflation,
reverse• Positive feedback leads to wild swings• Need central banking control
• Mandelbrot & Hudson [2004]– Fractal models
• Better description of real market swings
Fat Tails• Investors tend to assume normal distribution
– Real investment data bell shaped– Normal distribution well-developed, widely understood
• TALEB [2007]– BLACK SWANS– Humans tend to assume if they haven’t seen it, it’s impossible
• BUT REAL INVESTMENT DATA OFF AT EXTREMES– Rare events have higher probability of occurring than normal
distribution would imply• Power-Log distribution• Student-t• Logistic• Normal
Human Cognitive Psychology
• Kahneman & Tversky [many – c. 1980]– Human decision making fraught with biases
• Often lead to irrational choices• FRAMING – biased by recent observations
– Risk-averse if winning– Risk-seeking if losing
• RARE EVENTS – we overestimate probability of rare events
– We fear the next asteroid– Airline security processing
Animal Spirits
• Akerlof & Shiller [2009]– Standard economic theory makes too many
assumptions• Decision makers consider all available options• Evaluate outcomes of each option
– Advantages, probabilities• Optimize expected results
– Akerlof & Shiller propose • Consideration of objectives in addition to profit• Altruism - fairness
Warren Buffett
• Conservative investment view– There is an underlying worth (value) to each firm– Stock market prices vary from that worth– BUY UNDERPRICED FIRMS– HOLD
• At least until your confidence is shaken
– ONLY INVEST IN THINGS YOU UNDERSTAND
• NOT INCOMPATIBLE WITH EMT
George Soros• Humans fallable• Bubbles examples reflexivity
– Human decisions affect data they analyze for future decisions
– Human nature to join the band-wagon– Causes bubble– Some shock brings down prices
• JUMP ON INITIAL BUBBLE-FORMING INVESTMENT OPPORTUNITIES– Help the bubble along– WHEN NEAR BURSTING, BAIL OUT
Nassim Taleb
• Black Swans– Human fallability in cognitive understanding– Investors considered successful in bubble-forming
period are headed for disaster• BLOW-Ups
• There is no profit in joining the band-wagon– Seek investments where everyone else is wrong
• Seek High-payoff on these long shots– Lottery-investment approach
• Except the odds in your favor
Taleb Statistical View
• Mathematics– Fair coin flips have a 50/50 probability of heads or
tails– If you observe 99 heads in succession, probability of
heads on next toss = 0.5• CASINO VIEW
– If you observe 99 heads in succession, probably the flipper is crooked
• MAKE SURE STATISTICS ARE APPROPRIATE TO DECISION
CASINO RISK
• Have game outcomes down to a science• ACTUAL DISASTERS
1. A tiger bit Siegfried or Roy – loss about $100 million2. A contractor suffered in constructing a hotel annex,
sued, lost – tried to dynamite casino3. Casinos required to file with Internal Revenue
Service – an employee failed to do that for years – Casino had to pay huge fine (risked license)
4. Casino owner’s daughter kidnapped – he violated gambling laws to use casino money to raise ransom
Risk Management Tools
• Simulation (Beneda [2005])– Monte Carlo – Crystal Ball
• Multiple criteria analysis– Tradeoffs between risk & return
• Balanced Scorecard– Organizational performance measurement
40Finland May 2010